### 文献
agentUniverse项目基于以下的研究成果支撑。

BibTeX formatted
```text
@misc{wang2024peerexpertizingdomainspecifictasks,
      title={PEER: Expertizing Domain-Specific Tasks with a Multi-Agent Framework and Tuning Methods}, 
      author={Yiying Wang and Xiaojing Li and Binzhu Wang and Yueyang Zhou and Han Ji and Hong Chen and Jinshi Zhang and Fei Yu and Zewei Zhao and Song Jin and Renji Gong and Wanqing Xu},
      year={2024},
      eprint={2407.06985},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2407.06985}, 
}
```
文献简介：该文献在研究实验部分分别从**完整性、相关性、紧凑性、事实性、逻辑性、结构性和全面性七个维度进行打分（各纬度满分为5分）**，**PEER模式在每个测评维度的平均分数均高于BabyAGI**，且在**完整性、相关性、逻辑性、结构性和全面性五个纬度有显著优势**；同时PEER模式在 GPT-3.5 turbo (16k) 模型下相较于 BabyAGI 的择优胜率达到 83%，在 GPT-4o 模型下择优胜率达到 81%，更多详情请阅读文献。
https://arxiv.org/pdf/2407.06985
